The Modeling Core will integrate data from Projects 1 and 2 and construct a joint host and pathogen gene regulatory network (GRN) model by regression-based inference of direct and indirect inter-organismal influences on gene expression. This cross-species GRN model will link quantitative phenotypes to causal environmental and genetic triggers that influence the outcome of TB infection, specifically susceptibility, resistance, infection progression, persistence, and clearance. Central to these predictive models are machine learning algorithms - cMonkey and Inferelator - that will work in tandem to discover groups of genes that are conditionally co-regulated by combinatorial environmental (e.g. pH) and genetic (e.g; transcription factors) influences. The data integration strategies incorporated into these algorithms will overcome several challenges: (1) technical and biological noise in systems biology data; (2) lack of functional information for over 50% of all genes in the genome; (3) lack of detailed knowledge of regulatory mechanisms; and (4) incomplete knowledge of the environmental space to which both the host and MTB networks have adapted. The Modeling Core will reverse engineer the architecture of GRNs directly from data while simultaneously learning the associated dynamics of transcriptional regulation. Moreover, genes of unknown function will be integrated into the network based on their co-expression patterns, and other shared features such as interactions, phylogeny, and cis-regulatory elements; making it possible to discover additional genes that might be critical for outcome of infection. Preliminary GRN models for both MTB and BMMO have already been constructed, and analysis of these models has demonstrated that they recapitulate existing knowledge and predict new genes that might, influence the outcome of MTB infection. These model predictions have provided guidance for experimental designs and priorities in both Projects.